Eaton India Innovation Center Accelerates Solar Energy Predictions with MATLAB
Low-Code AI Apps and Custom Tools Streamline Photovoltaic Forecasting and Boost System Performance
“We can leverage the AI toolchain of MathWorks for superior PV energy predictions through a user-friendly approach, through a GUI, through App Designer.”
Key Outcomes
- MATLAB enabled the import and preprocessing of large data sets with PV system data, ensuring it was clean and structured for analysis
- MATLAB AI apps were used to develop and train machine learning models tailored for PV energy prediction, facilitating an interactive and user-friendly approach to model creation
- Trained models were deployed to edge devices, using Deep Learning Toolbox™ tools for AI compression and TinyML techniques to ensure efficient operation on resource-constrained hardware
Engineering teams at Eaton India Innovation Center research and develop products that improve the performance and reliability of solar power systems through advanced energy predictions. Accurately forecasting photovoltaic (PV) energy output is critical, but traditional data handling and AI model training methods are often complex and time-consuming.
The engineering teams simplified this process by using MATLAB® low-code AI apps to import, preprocess, and analyze data, making it possible to deploy predictive models efficiently. They also developed a custom app with MATLAB App Designer, enabling users to easily train and deploy PV energy prediction models. Finally, they explored edge deployment and AI compression techniques to further enhance system performance.
